# Questions tagged [reference-request]

Use when requesting examples of research or research papers, books, articles, blog posts or courses. For example, "Is there any published research about X?" or "What are good examples of Y in research?".

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### Is there a standardized method to train a reinforcement learning NN by demonstration?

I'm less familiar with reinforcement learning compared to other neural network learning approaches, so I'm unaware of anything exactly like what I want for an approach. I'm wondering if there are any ...
1 vote
22 views

### Does LSTM provide any unique value or advantages compared to other algorithms, including "vanilla" RNN?

I have heard a lot of hype around LSTM for all kinds of time-series based applications including NLP. Despite this, I haven't seen many (if any) applications of LSTM where LSTM performs uniquely well ...
8 views

### Help finding a recent paper describing how current DL methods are not inspired by biology

I know this is a very long shot, but about a month ago I came across a paper describing how current DL architectures are not inspired by biology, and how the fact that most research only aims to push ...
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1 vote
24 views

### Today's Practicality of Bayesian Neural Networks

Just having heard lately about BNNs (wow, ANNs and CNNs are clear; now there's a B? What's that? Ahh, Bayesian ;-)) and quickly getting their main idea and focus, that is, weights not being pure ...
• 111
16 views

### Deriving the cross entropy loss via maximum-likelihood estimation?

For multi-class classification problems, we use the cross entropy loss, which can be derived from a multinomial distribution via the maximum likelihoos estimation method. I've already tried to derive ...
• 121
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### How to combine data measurements with underlying model? [closed]

The problem I am trying to solve is this: Imagine you are in the situation where you want to predict car performance according to some characteristics (horse power, car dimensions, etc...). The data ...
30 views

### Is Bayesian Reinforcement Learning used as off-policy RL?

Are there any examples where Bayesian Reinforcement Learning is used as off-policy RL? What are the pros and cons of using it for this purpose?
24 views

### Are there more optimization methods like GAE for PPO [closed]

I posted about this earlier, but got the suggestion to separate the questions. I'm currently trying to "solve" the OpenAI gym "Humanoid" environment. To improve the training ...
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14 views

### How do we call a transformer having N encoders and M decoders and a learnable cross-connectivity between encoders and decoders?

How do we call a transformer having N encoders and M decoders and a learnable cross-connectivity between encoders and decoders? I am interested particularly in the case when M=1, but I imagine that it ...
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1 vote
41 views

### Current state of the art and datasets for combining NLP and CV?

I was considering a scenario where natural language processing (NLP) and computer vision (CV) are combined, for example in extended reality systems that get as input both natural language and non-...
• 121
18 views

### Can machine learning algorithms automatically create formulations of optimizing algorithms?

Suppose we want to create an optimization algorithm which should be able to find an optimum value for non-convex optimization problems. Usually meta-heuristics are used for this purpose. Designing a ...
45 views

### Does pairing children with their parents cause any harm (in a genetic program)?

If you pair parents with their children (with a cross-over) does this prevent making individuals which are more fit or does this cause other side effects which are harmful to the genetic process? I ...
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47 views

### Is there any variant of perceptron convergence algorithm that ensures uniqueness?

The perceptron convergence algorithm given below ensures the convergence of weights of the perceptron provided enough data points and iterations. Although it ensures convergence by finally getting a ...
• 3,099
17 views

### Why does triplet loss allow to learn a ranking whereas contrastive loss only allows to learn similarity?

I am looking at this lecture, which states (link to exact time): What the triplet loss allows us in contrast to the contrastive loss is that we can learn a ranking. So it's not only about similarity, ...
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34 views

### What are some solid metrics to evaluate/compare the outputs of explainable algorithms?

Consider a learned CNN image classifier and a task that focuses on studying the outputs of explainable algorithms, such as integrated gradients and grad-cam, on the classifier's predictions. I am ...
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### What are knowledge graph embeddings?

What are knowledge graph embeddings? How are they useful? Are there any extensive reviews on the subject to know all the details? Note that I am asking this question just to give a quick overview of ...
• 33.8k
50 views

### How do we give recommendations when users create/post content (like in YouTube)?

I've explored tools like amazon personalize, etc. for generating recommendations. It seems like amazon personalize is appropriate when all the content is with the company/a single entity. For example, ...
39 views

### Algorithms for solving Contextual Bandits Problem with multiples continuous actions

I am currently working on a problem that has 7 continuous actions and instantly gives a reward. I was thinking that there are Contextual-Bandits-Algorithms applicable to this kind of problem, but so ...
1 vote
74 views

### How to train an ML model to convert the given lyrics into a song by a particular singer?

I am interested in training a machine algorithm to convert the lyrics I give into a song by a particular singer. My language is non-English (south Indian) The songs are mostly monophonic (very few ...
• 15
1 vote
10 views

### How does the distribution of the parameters change in logistic regression?

I have my own data to train a logistic regression model (for a multi-class classification task), and I want to know how the distribution of weight parameters changes after each update with gradient ...
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